Real-Life Cases
Healthcare: According to Obermeyer et al. (2019) machine
learning encounters significant obstacles when used in
healthcare because training data proves to be biased. New
methods were developed to make data more diverse while
achieving fair models.
Natural Language Processing: Gururangan et al. (2018)
investigated how label noise affects sentiment analysis.
The study examined methods that could boost model
performance through better label consistency techniques.
References
Gururangan, S., Marasović, A., Swayamdipta, S.,
et al. (2018). Annotation artifacts in natural
language inference data. Proceedings of the
2018 Conference of the North American
Chapter of the Association for Computational
Linguistics: Human Language Technologies,
2018, 1-6.
Obermeyer, Z., Powers, B., Vogeli, C., &
Mullainathan, S. (2019). Dissecting racial bias
in an algorithm used to manage the health of
populations. Science, 366(6464), 447-453.
https://doi.org/10.1126/science.aax2342